近年來，次世代定序（Next-Generation Sequencing）技術與第三代定序技術的發展徹底改變了基因體研究的面貌。與傳統的桑格定序方法相比，NGS 技術具有更高的通量、更低的成本和更短的計算時間，使研究人員能夠以前所未有的規模和深度探索基因體。而在其中的變異檢測是一項相當複雜的任務，基因變異包括單核苷酸變異（SNP）、插入缺失（InDel）、拷貝數變異（CNV）等多種類型，不同類型的變異具有不同的檢測難度。因此變異檢測算法需要對大量的基因體資料進行分析，因此計算量非常龐大。
為了克服這個困難，並提高變異檢測效率， 本文旨在深入研究如何應用 FPGA 的系統與模組，以加速深度學習模型在 DeepVariant 中 variant calling 這個步驟的推理過程。我們將探討整合過程中所面臨的具體問題，包括硬體與軟體的整合、效能最佳化等方向。此外，也將對加速後的結果進行詳細分析，並與未加速的 CPU 版本進行對比，以評估加速效果及效能提升情況。

In recent years, the development of Next-Generation Sequencing (NGS) technologyhas dramatically transformed the landscape of genomic research. Compared to traditional Sanger sequencing methods, NGS technology offers higher throughput, lower costs, and shorter computation times, allowing researchers to explore genomes on an unprecedented scale and depth. Among the tasks involved, variant detection is particularly complex, encompassing various types of genetic variations such as single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), and copy number variations (CNVs). Each type of variant presents different detection challenges, requiring variant detection algorithms to analyze vast amounts of genomic data, leading to significant computational demands.
To overcome these challenges and improve the efficiency of variant detection, this study aims to explore how to apply FPGA systems and modules to accelerate the inference process of deep learning models in the variant calling step of DeepVariant. We will investigate the specific issues encountered during the integration process, including hardware and software integration and performance optimization. Additionally, we will conduct a detailed analysis of the results post-acceleration and compare them with the unaccelerated CPU version to evaluate the effectiveness and performance improvements of the acceleration.

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